Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Explain with AI

        Configure AI settings to get explanations of plots and data in this report.

        Keys entered here will be stored in your browser's local storage. See the docs.


        Anonymize samples off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


        Choose Plots

        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
        Settings are automatically saved. You can also save named configurations below.

        Save Settings

        You can save the toolbox settings for this report to the browser or as a file.


        Load Settings

        Choose a saved report profile from the browser or load from a file:

          Load from File

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.28

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-05-14, 11:11 BST based on data in: /nobackup/gdmn373/rnaseq/work/db/19a123cb885b5eea6c5e3c7d7a41be

        General Statistics

        Showing 60/60 rows and 9/16 columns.
        Sample NameAlignableProper PairsTotal readsAlignedAlignedUniq alignedUniq alignedMultimappedTrimmed basesrRNADupsGCAvg lenMedian lenFailedSeqs
        Camb4_P
        100.0%
        87.1%
        78.6M
        78.3M
        99.6%
        75.1M
        95.6%
        3.1M
        1.7%
        Camb4_P_raw_1
        10.8%
        41.8%
        43.0%
        151bp
        151bp
        18%
        80.7M
        Camb4_P_raw_2
        11.0%
        39.4%
        43.0%
        151bp
        151bp
        27%
        80.7M
        Camb4_P_val_1
        41.5%
        42.0%
        135bp
        147bp
        18%
        80.3M
        Camb4_P_val_2
        39.1%
        42.0%
        135bp
        147bp
        18%
        80.3M
        Camb4_R
        100.0%
        86.1%
        67.8M
        67.6M
        99.6%
        64.8M
        95.5%
        2.8M
        1.9%
        Camb4_R_raw_1
        9.7%
        34.2%
        43.0%
        151bp
        151bp
        18%
        69.9M
        Camb4_R_raw_2
        10.0%
        31.5%
        43.0%
        151bp
        151bp
        27%
        69.9M
        Camb4_R_val_1
        33.9%
        42.0%
        137bp
        147bp
        18%
        69.6M
        Camb4_R_val_2
        31.3%
        42.0%
        137bp
        147bp
        18%
        69.6M
        Camb7_P
        100.0%
        86.8%
        47.8M
        47.7M
        99.7%
        45.7M
        95.6%
        1.9M
        1.9%
        Camb7_P_raw_1
        10.7%
        27.1%
        43.0%
        151bp
        151bp
        18%
        49.2M
        Camb7_P_raw_2
        10.9%
        25.9%
        43.0%
        151bp
        151bp
        27%
        49.2M
        Camb7_P_val_1
        26.8%
        42.0%
        135bp
        147bp
        18%
        49.0M
        Camb7_P_val_2
        25.7%
        42.0%
        135bp
        147bp
        18%
        49.0M
        Camb7_R
        100.0%
        89.8%
        50.2M
        50.0M
        99.6%
        48.3M
        96.2%
        1.7M
        1.7%
        Camb7_R_raw_1
        9.3%
        27.1%
        42.0%
        151bp
        151bp
        9%
        51.4M
        Camb7_R_raw_2
        9.5%
        26.2%
        42.0%
        151bp
        151bp
        27%
        51.4M
        Camb7_R_val_1
        27.0%
        41.0%
        137bp
        147bp
        9%
        51.3M
        Camb7_R_val_2
        26.1%
        41.0%
        137bp
        147bp
        18%
        51.3M
        Camb8_P
        100.0%
        86.7%
        48.4M
        48.1M
        99.5%
        46.2M
        95.4%
        2.0M
        1.9%
        Camb8_P_raw_1
        11.4%
        29.9%
        43.0%
        151bp
        151bp
        18%
        49.8M
        Camb8_P_raw_2
        11.7%
        28.7%
        43.0%
        151bp
        151bp
        36%
        49.8M
        Camb8_P_val_1
        29.6%
        42.0%
        134bp
        147bp
        18%
        49.6M
        Camb8_P_val_2
        28.4%
        42.0%
        134bp
        147bp
        27%
        49.6M
        Camb8_R
        100.0%
        86.8%
        54.0M
        53.7M
        99.4%
        51.5M
        95.3%
        2.2M
        2.0%
        Camb8_R_raw_1
        9.6%
        32.0%
        43.0%
        151bp
        151bp
        18%
        55.8M
        Camb8_R_raw_2
        9.9%
        30.7%
        43.0%
        151bp
        151bp
        36%
        55.8M
        Camb8_R_val_1
        31.7%
        42.0%
        137bp
        147bp
        18%
        55.5M
        Camb8_R_val_2
        30.4%
        42.0%
        137bp
        147bp
        27%
        55.5M
        Camb9_P
        100.0%
        85.2%
        61.9M
        61.7M
        99.6%
        59.0M
        95.2%
        2.7M
        2.0%
        Camb9_P_raw_1
        9.2%
        30.2%
        43.0%
        151bp
        151bp
        18%
        63.9M
        Camb9_P_raw_2
        9.6%
        29.0%
        43.0%
        151bp
        151bp
        36%
        63.9M
        Camb9_P_val_1
        30.0%
        43.0%
        137bp
        147bp
        18%
        63.6M
        Camb9_P_val_2
        28.8%
        43.0%
        137bp
        147bp
        27%
        63.6M
        Camb9_R
        100.0%
        85.6%
        60.4M
        60.2M
        99.7%
        57.6M
        95.4%
        2.6M
        2.0%
        Camb9_R_raw_1
        6.7%
        26.8%
        43.0%
        151bp
        151bp
        18%
        62.3M
        Camb9_R_raw_2
        7.2%
        25.9%
        43.0%
        151bp
        151bp
        36%
        62.3M
        Camb9_R_val_1
        26.7%
        43.0%
        141bp
        151bp
        18%
        62.1M
        Camb9_R_val_2
        25.9%
        43.0%
        141bp
        151bp
        27%
        62.1M
        Camb10_P
        100.0%
        80.2%
        25.3M
        25.2M
        99.4%
        23.7M
        93.5%
        1.5M
        2.4%
        Camb10_P_raw_1
        11.3%
        39.8%
        45.0%
        151bp
        151bp
        27%
        26.7M
        Camb10_P_raw_2
        11.6%
        37.2%
        45.0%
        151bp
        151bp
        36%
        26.7M
        Camb10_P_val_1
        38.6%
        45.0%
        136bp
        147bp
        18%
        26.1M
        Camb10_P_val_2
        36.0%
        45.0%
        136bp
        147bp
        18%
        26.1M
        Camb10_R
        100.0%
        81.1%
        61.5M
        61.2M
        99.5%
        57.8M
        94.0%
        3.4M
        2.2%
        Camb10_R_raw_1
        9.4%
        28.7%
        44.0%
        151bp
        151bp
        18%
        63.5M
        Camb10_R_raw_2
        9.6%
        27.7%
        44.0%
        151bp
        151bp
        27%
        63.5M
        Camb10_R_val_1
        28.5%
        44.0%
        137bp
        147bp
        9%
        63.3M
        Camb10_R_val_2
        27.5%
        44.0%
        137bp
        147bp
        18%
        63.3M
        Camb13_P
        100.0%
        88.5%
        61.2M
        61.0M
        99.7%
        58.7M
        96.0%
        2.2M
        1.8%
        Camb13_P_raw_1
        10.5%
        31.2%
        42.0%
        151bp
        151bp
        18%
        62.7M
        Camb13_P_raw_2
        11.0%
        28.5%
        42.0%
        151bp
        151bp
        36%
        62.7M
        Camb13_P_val_1
        31.2%
        42.0%
        135bp
        147bp
        18%
        62.6M
        Camb13_P_val_2
        28.5%
        41.0%
        135bp
        147bp
        27%
        62.6M
        Camb13_R
        100.0%
        88.5%
        68.5M
        68.2M
        99.6%
        65.8M
        96.0%
        2.5M
        1.8%
        Camb13_R_raw_1
        9.9%
        33.5%
        42.0%
        151bp
        151bp
        18%
        70.3M
        Camb13_R_raw_2
        10.4%
        30.4%
        42.0%
        151bp
        151bp
        36%
        70.3M
        Camb13_R_val_1
        33.5%
        41.0%
        136bp
        147bp
        18%
        70.2M
        Camb13_R_val_2
        30.4%
        41.0%
        136bp
        147bp
        27%
        70.2M

        RSEM

        Estimates gene and isoform expression levels from RNA-Seq data.URL: https://deweylab.github.io/RSEMDOI: 10.1186/1471-2105-12-323

        Mapped Reads

        A breakdown of how all reads were aligned for each sample.

        Created with MultiQC

        Multimapping rates

        A frequency histogram showing how many reads were aligned to n reference regions.

        In an ideal world, every sequence reads would align uniquely to a single location in the reference. However, due to factors such as repeititve sequences, short reads and sequencing errors, reads can be align to the reference 0, 1 or more times. This plot shows the frequency of each factor of multimapping. Good samples should have the majority of reads aligning once.

        Created with MultiQC

        RSeQC

        Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

        Read Distribution

        Read Distribution calculates how mapped reads are distributed over genome features.

        Created with MultiQC

        Read Duplication

        read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

        Created with MultiQC

        Infer experiment

        Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

        Created with MultiQC

        Bam Stat

        All numbers reported in millions.

        Created with MultiQC

        STAR

        Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

        Summary Statistics

        Summary statistics from the STAR alignment

        Showing 12/12 rows and 10/19 columns.
        Sample NameTotal readsAlignedAlignedUniq alignedUniq alignedMultimappedAvg. read lenAvg. mapped lenSplicesAnnotated splicesGT/AG splicesGC/AG splicesAT/AC splicesNon-canonical splicesMismatch rateDel rateDel lenIns rateIns len
        Camb4_P
        78.6M
        78.3M
        99.6%
        75.1M
        95.6%
        3.1M
        270.0bp
        267.9bp
        9.3M
        9.3M
        9.1M
        0.1M
        0.0M
        0.1M
        0.5%
        0.1%
        2.2bp
        0.0%
        1.6bp
        Camb4_R
        67.8M
        67.6M
        99.6%
        64.8M
        95.5%
        2.8M
        273.0bp
        270.7bp
        6.7M
        6.7M
        6.6M
        0.1M
        0.0M
        0.0M
        0.6%
        0.1%
        2.1bp
        0.0%
        1.6bp
        Camb7_P
        47.8M
        47.7M
        99.7%
        45.7M
        95.6%
        1.9M
        270.0bp
        267.9bp
        6.3M
        6.3M
        6.2M
        0.1M
        0.0M
        0.0M
        0.6%
        0.1%
        2.2bp
        0.0%
        1.6bp
        Camb7_R
        50.2M
        50.0M
        99.6%
        48.3M
        96.2%
        1.7M
        274.0bp
        271.6bp
        7.0M
        7.0M
        6.9M
        0.1M
        0.0M
        0.0M
        0.4%
        0.1%
        2.2bp
        0.0%
        1.6bp
        Camb8_P
        48.4M
        48.1M
        99.5%
        46.2M
        95.4%
        2.0M
        268.0bp
        265.2bp
        6.0M
        6.0M
        5.9M
        0.1M
        0.0M
        0.0M
        0.5%
        0.1%
        2.2bp
        0.0%
        1.6bp
        Camb8_R
        54.0M
        53.7M
        99.4%
        51.5M
        95.3%
        2.2M
        273.0bp
        271.3bp
        8.3M
        8.3M
        8.1M
        0.1M
        0.0M
        0.0M
        0.4%
        0.1%
        2.1bp
        0.0%
        1.6bp
        Camb9_P
        61.9M
        61.7M
        99.6%
        59.0M
        95.2%
        2.7M
        274.0bp
        272.3bp
        6.4M
        6.4M
        6.3M
        0.1M
        0.0M
        0.0M
        0.5%
        0.1%
        2.0bp
        0.0%
        1.6bp
        Camb9_R
        60.4M
        60.2M
        99.7%
        57.6M
        95.4%
        2.6M
        281.0bp
        279.4bp
        9.7M
        9.7M
        9.6M
        0.1M
        0.0M
        0.0M
        0.4%
        0.1%
        2.1bp
        0.0%
        1.6bp
        Camb10_P
        25.3M
        25.2M
        99.4%
        23.7M
        93.5%
        1.5M
        272.0bp
        268.8bp
        2.9M
        2.9M
        2.8M
        0.1M
        0.0M
        0.0M
        0.5%
        0.1%
        2.1bp
        0.0%
        1.6bp
        Camb10_R
        61.5M
        61.2M
        99.5%
        57.8M
        94.0%
        3.4M
        273.0bp
        271.4bp
        8.6M
        8.6M
        8.5M
        0.1M
        0.0M
        0.1M
        0.5%
        0.1%
        2.1bp
        0.0%
        1.6bp
        Camb13_P
        61.2M
        61.0M
        99.7%
        58.7M
        96.0%
        2.2M
        269.0bp
        266.8bp
        8.8M
        8.8M
        8.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.1%
        2.2bp
        0.0%
        1.6bp
        Camb13_R
        68.5M
        68.2M
        99.6%
        65.8M
        96.0%
        2.5M
        271.0bp
        268.7bp
        9.1M
        9.1M
        9.0M
        0.1M
        0.0M
        0.0M
        0.5%
        0.1%
        2.2bp
        0.0%
        1.6bp

        Alignment Scores

        Created with MultiQC

        Cutadapt

        Version: 4.9

        Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

        Created with MultiQC

        Trimmed Sequence Lengths (3')

        This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        Created with MultiQC

        SortMeRNA

        Program for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data.URL: http://bioinfo.lifl.fr/RNA/sortmernaDOI: 10.1093/bioinformatics/bts611

        The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data.

        Created with MultiQC

        FastQC

        Version: 0.12.1

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 11/11 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCG
        10
        2093662
        0.0743%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTTCAGATCTCGTATGC
        1
        152296
        0.0054%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACGACTCAATCTCGTATGC
        1
        209519
        0.0074%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTTGTGATCTCGTATGC
        1
        440654
        0.0156%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACTGTGGTATCTCGTATGC
        1
        169550
        0.0060%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTGGTATCTCGTATGC
        1
        358211
        0.0127%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACCCTAAGATCTCGTATGC
        1
        228499
        0.0081%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTCAGAATCTCGTATGC
        1
        174699
        0.0062%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACACCGATATCTCGTATGC
        1
        128721
        0.0046%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTAAGCATCTCGTATGC
        1
        64344
        0.0023%
        GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATTGGATCTCGTATGC
        1
        73561
        0.0026%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        Cutadapt4.9
        FastQC0.12.1